{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "##取出TP地区的数据\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "\n",
    "path='/data1/Reanalysis/ERA5/tcw/'\n",
    "year=np.arange(1979,2019,1)\n",
    "print(year)\n",
    "\n",
    "filelist=sorted(glob.glob(path+'ERA5_tcw*.nc'))\n",
    "for i in range(len(filelist)):\n",
    "    print(filelist[i])\n",
    "\n",
    "for i in range(len(filelist)):\n",
    "    print(filelist[i])\n",
    "    ds=xr.open_dataset(filelist[i])\n",
    "    tcw=ds['tcw'][:,200:261,280:421]\n",
    "    print(tcw)\n",
    "    tcw.rename('tcw').to_netcdf('TP_ERA5_tcw_'+str(year[i])+'.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "##hourly to daily\n",
    "from cdo import *\n",
    "import glob\n",
    "cdo   = Cdo() \n",
    "\n",
    "filelist=glob.glob('./TP_ERA5_tcw_*.nc')\n",
    "for i in range(len(filelist)):\n",
    "    cdo.daymean(input=filelist[i],output=filelist[i][0:-3]+'_daily.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "from rasterio import features\n",
    "from affine import Affine\n",
    "\n",
    "def transform_from_latlon(lat, lon):\n",
    "    \"\"\" input 1D array of lat / lon and output an Affine transformation\n",
    "    \"\"\"\n",
    "    lat = np.asarray(lat)\n",
    "    lon = np.asarray(lon)\n",
    "    trans = Affine.translation(lon[0], lat[0])\n",
    "    scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])\n",
    "    return trans * scale\n",
    "\n",
    "def rasterize(shapes, coords, latitude='latitude', longitude='longitude',\n",
    "              fill=np.nan, **kwargs):\n",
    "    \"\"\"Rasterize a list of (geometry, fill_value) tuples onto the given\n",
    "    xray coordinates. This only works for 1d latitude and longitude\n",
    "    arrays.\n",
    "\n",
    "    usage:\n",
    "    -----\n",
    "    1. read shapefile to geopandas.GeoDataFrame\n",
    "          `states = gpd.read_file(shp_dir+shp_file)`\n",
    "    2. encode the different shapefiles that capture those lat-lons as different\n",
    "        numbers i.e. 0.0, 1.0 ... and otherwise np.nan\n",
    "          `shapes = (zip(states.geometry, range(len(states))))`\n",
    "    3. Assign this to a new coord in your original xarray.DataArray\n",
    "          `ds['states'] = rasterize(shapes, ds.coords, longitude='X', latitude='Y')`\n",
    "\n",
    "    arguments:\n",
    "    ---------\n",
    "    : **kwargs (dict): passed to `rasterio.rasterize` function\n",
    "\n",
    "    attrs:\n",
    "    -----\n",
    "    :transform (affine.Affine): how to translate from latlon to ...?\n",
    "    :raster (numpy.ndarray): use rasterio.features.rasterize fill the values\n",
    "      outside the .shp file with np.nan\n",
    "    :spatial_coords (dict): dictionary of {\"X\":xr.DataArray, \"Y\":xr.DataArray()}\n",
    "      with \"X\", \"Y\" as keys, and xr.DataArray as values\n",
    "\n",
    "    returns:\n",
    "    -------\n",
    "    :(xr.DataArray): DataArray with `values` of nan for points outside shapefile\n",
    "      and coords `Y` = latitude, 'X' = longitude.\n",
    "\n",
    "\n",
    "    \"\"\"\n",
    "    transform = transform_from_latlon(coords[latitude], coords[longitude])\n",
    "    out_shape = (len(coords[latitude]), len(coords[longitude]))\n",
    "    raster = features.rasterize(shapes, out_shape=out_shape,\n",
    "                                fill=fill, transform=transform,\n",
    "                                dtype=float, **kwargs)\n",
    "    spatial_coords = {latitude: coords[latitude], longitude: coords[longitude]}\n",
    "    return xr.DataArray(raster, coords=spatial_coords, dims=(latitude, longitude))\n",
    "\n",
    "def add_shape_coord_from_data_array(xr_da, shp_path, coord_name):\n",
    "    \"\"\" Create a new coord for the xr_da indicating whether or not it \n",
    "         is inside the shapefile\n",
    "\n",
    "        Creates a new coord - \"coord_name\" which will have integer values\n",
    "         used to subset xr_da for plotting / analysis/\n",
    "\n",
    "        Usage:\n",
    "        -----\n",
    "        precip_da = add_shape_coord_from_data_array(precip_da, \"awash.shp\", \"awash\")\n",
    "        awash_da = precip_da.where(precip_da.awash==0, other=np.nan) \n",
    "    \"\"\"\n",
    "    # 1. read in shapefile\n",
    "    shp_gpd = gpd.read_file(shp_path)\n",
    "\n",
    "    # 2. create a list of tuples (shapely.geometry, id)\n",
    "    #    this allows for many different polygons within a .shp file (e.g. States of US)\n",
    "    shapes = [(shape, n) for n, shape in enumerate(shp_gpd.geometry)]\n",
    "\n",
    "    # 3. create a new coord in the xr_da which will be set to the id in `shapes`\n",
    "    xr_da[coord_name] = rasterize(shapes, xr_da.coords, \n",
    "                               longitude='longitude', latitude='latitude')\n",
    "\n",
    "    return xr_da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'tcw' (time: 14610, latitude: 61, longitude: 141)>\n",
      "array([[[ 7.8563766,  7.2692947,  7.465721 , ...,  4.797386 ,\n",
      "          4.788559 ,  4.7951813],\n",
      "        [ 3.4157639,  2.961113 ,  2.910347 , ...,  4.9916077,\n",
      "          4.978363 ,  5.053406 ],\n",
      "        [ 2.502037 ,  2.3166504,  2.2371902, ...,  5.1063766,\n",
      "          5.190239 ,  5.2388   ],\n",
      "        ...,\n",
      "        [ 9.343933 ,  8.87162  ,  8.386066 , ..., 11.654728 ,\n",
      "         13.285748 , 14.532738 ],\n",
      "        [ 9.156334 ,  8.701679 ,  8.24482  , ..., 12.71191  ,\n",
      "         14.17078  , 16.084301 ],\n",
      "        [ 8.902519 ,  8.480972 ,  8.189636 , ..., 14.537151 ,\n",
      "         16.06223  , 18.375233 ]],\n",
      "\n",
      "       [[ 6.1503143,  5.6559296,  5.7375946, ...,  3.0206985,\n",
      "          3.0140762,  3.0162888],\n",
      "        [ 3.1994705,  2.7911682,  2.72937  , ...,  3.2060928,\n",
      "          3.1663666,  3.21492  ],\n",
      "        [ 2.3012009,  2.133461 ,  2.0451813, ...,  3.331894 ,\n",
      "          3.393692 ,  3.4334183],\n",
      "        ...,\n",
      "        [10.637272 , 10.279728 ,  9.931011 , ..., 11.853363 ,\n",
      "         13.001034 , 13.656532 ],\n",
      "        [10.42981  , 10.070057 ,  9.730167 , ..., 12.10276  ,\n",
      "         13.100353 , 14.464317 ],\n",
      "        [10.198067 ,  9.860386 ,  9.602161 , ..., 13.230568 ,\n",
      "         14.389278 , 16.188034 ]],\n",
      "\n",
      "       [[ 6.4217834,  6.1481094,  6.7373962, ...,  4.614197 ,\n",
      "          4.567848 ,  4.5104675],\n",
      "        [ 3.3804474,  3.007454 ,  2.9655228, ...,  4.8062134,\n",
      "          4.7311707,  4.737793 ],\n",
      "        [ 2.5285263,  2.3983078,  2.3343048, ...,  4.9209824,\n",
      "          4.9607086,  4.969536 ],\n",
      "        ...,\n",
      "        [ 9.430008 ,  9.449871 ,  9.485184 , ...,  9.869213 ,\n",
      "         11.286148 , 12.244015 ],\n",
      "        [ 9.421181 ,  9.527119 ,  9.59333  , ...,  9.953083 ,\n",
      "         11.074268 , 12.700874 ],\n",
      "        [ 9.489601 ,  9.622025 ,  9.789761 , ..., 10.749832 ,\n",
      "         11.871021 , 13.897102 ]],\n",
      "\n",
      "       ...,\n",
      "\n",
      "       [[ 7.3863983,  6.958103 ,  6.995777 , ...,  2.411419 ,\n",
      "          2.3261566,  2.225029 ],\n",
      "        [ 5.0406837,  4.5588493,  4.4894485, ...,  2.5442696,\n",
      "          2.4490929,  2.379692 ],\n",
      "        [ 3.7557945,  3.5257835,  3.448452 , ...,  2.691002 ,\n",
      "          2.6017723,  2.5522003],\n",
      "        ...,\n",
      "        [ 9.482273 ,  9.218555 ,  8.875519 , ..., 15.357468 ,\n",
      "         16.202164 , 16.82478  ],\n",
      "        [ 9.337524 ,  9.071823 ,  8.716892 , ..., 16.541233 ,\n",
      "         17.27687  , 17.917332 ],\n",
      "        [ 9.226486 ,  8.962765 ,  8.742668 , ..., 17.947075 ,\n",
      "         18.504257 , 19.12489  ]],\n",
      "\n",
      "       [[ 8.782326 ,  8.001083 ,  8.498779 , ...,  2.4272804,\n",
      "          2.4570236,  2.4748688],\n",
      "        [ 4.0294266,  3.1649017,  3.1430893, ...,  2.4847832,\n",
      "          2.5065956,  2.5561676],\n",
      "        [ 2.6255646,  2.4193497,  2.3479652, ...,  2.5621147,\n",
      "          2.5839272,  2.6553078],\n",
      "        ...,\n",
      "        [ 9.061909 ,  8.788277 ,  8.5424   , ...,  7.0731087,\n",
      "          7.8761635,  8.548351 ],\n",
      "        [ 8.867588 ,  8.635597 ,  8.463089 , ...,  8.1874695,\n",
      "          8.986561 ,  9.837204 ],\n",
      "        [ 8.88147  ,  8.701031 ,  8.633614 , ...,  9.599262 ,\n",
      "         10.511375 , 11.4373665]],\n",
      "\n",
      "       [[ 7.368553 ,  6.811371 ,  7.43795  , ...,  1.8205261,\n",
      "          1.7868195,  1.7987175],\n",
      "        [ 3.1649017,  2.7385902,  2.6691895, ...,  1.8899269,\n",
      "          1.8582039,  1.876049 ],\n",
      "        [ 2.4451256,  2.2547722,  2.16951  , ...,  1.9771729,\n",
      "          1.9652748,  1.9593277],\n",
      "        ...,\n",
      "        [ 7.362602 ,  7.1801796,  7.0691414, ...,  8.697063 ,\n",
      "          9.496155 , 10.130669 ],\n",
      "        [ 7.2793236,  7.1544037,  7.0631943, ..., 10.1445465,\n",
      "         10.911911 , 11.889458 ],\n",
      "        [ 7.227768 ,  7.120697 ,  7.134575 , ..., 12.058002 ,\n",
      "         12.867004 , 13.9655075]]], dtype=float32)\n",
      "Coordinates:\n",
      "  * latitude   (latitude) float32 40.0 39.75 39.5 39.25 ... 25.5 25.25 25.0\n",
      "  * longitude  (longitude) float32 70.0 70.25 70.5 70.75 ... 104.5 104.75 105.0\n",
      "  * time       (time) datetime64[ns] 1979-01-01T11:30:00 ... 2018-12-31T11:30:00\n",
      "Attributes:\n",
      "    long_name:  Total column water\n",
      "    units:      kg m**-2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fc1f0061410>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##选出TP的边界，和2500m以上的区域\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "\n",
    "ds=xr.open_mfdataset('./TP_ERA5_tcw_*_daily.nc',combine='by_coords')\n",
    "tcw=ds['tcw'].load()\n",
    "print(tcw)\n",
    "\n",
    "tcw_tp = add_shape_coord_from_data_array(tcw, './DBATP/DBATP_Polygon.shp', \"tp\")\n",
    "tcw_tp = tcw_tp.where(tcw_tp.tp==0, other=np.nan)\n",
    "tcw_tp[0,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fc1cc1662d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##挑出海拔2500m以上的区域\n",
    "de=xr.open_dataset('era5-orography.nc')\n",
    "z=de['z'][0,200:261,280:421]/9.8\n",
    "\n",
    "tcw_tp=tcw_tp.where(z>2500,other=np.nan)\n",
    "tcw_tp[0,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fc1cc0af1d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z.where(z>3000,other=np.nan).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcw_tp.rename('tcw_tp').to_netcdf('tp_tcw_2500m_1979_2018.nc','w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray &#x27;tcw&#x27; (time: 14610, latitude: 61, longitude: 141)&gt;\n",
       "array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 40.0 39.75 39.5 39.25 ... 25.5 25.25 25.0\n",
       "  * longitude  (longitude) float32 70.0 70.25 70.5 70.75 ... 104.5 104.75 105.0\n",
       "  * time       (time) datetime64[ns] 1979-01-01T11:30:00 ... 2018-12-31T11:30:00\n",
       "    tp         (latitude, longitude) float64 nan nan nan nan ... nan nan nan nan\n",
       "Attributes:\n",
       "    long_name:  Total column water\n",
       "    units:      kg m**-2</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tcw' (time: 14610, latitude: 61, longitude: 141)>\n",
       "array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float32 40.0 39.75 39.5 39.25 ... 25.5 25.25 25.0\n",
       "  * longitude  (longitude) float32 70.0 70.25 70.5 70.75 ... 104.5 104.75 105.0\n",
       "  * time       (time) datetime64[ns] 1979-01-01T11:30:00 ... 2018-12-31T11:30:00\n",
       "    tp         (latitude, longitude) float64 nan nan nan nan ... nan nan nan nan\n",
       "Attributes:\n",
       "    long_name:  Total column water\n",
       "    units:      kg m**-2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tcw_tp"
   ]
  }
 ],
 "metadata": {
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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